Interpreting Aristotle’s <i>Posterior Analytics</i> in Late Antiquity and Beyond
Bibliographic record
Abstract
Part I CONCEPT FORMATION IN POSTERIOR ANALYTICS II 19 1. The Ancient Commentators on Concept Formation Richard Sorabji 2. Proclus' Criticism of Aristotle'sTheory of Abstraction and Concept Formation in Analytica Posteriora II 19 Christoph Helmig 3. Eustratius' Comments on Posterior Analytics II 19 Katerina Ierodiakonou 4. Roger Bacon on Experiment, Induction and Intellect in his Reception of Analytica Posteriora II 19 Pia A. Antolic-Piper Part II METAPHYSICS AS A SCIENCE 5. Alexander of Aphrodisias on the Science of Ontology Maddalena Bonelli 6. Les Seconds Analytique' dans le commentaire de Syrianus sur la Metaphysique d'Aristote Angela Longo Part III DEMONSTRATION, DEFINITION AND CAUSATION 7. Alexander and Philoponus on Prior Analytics I 27-30: Is There Tension between Aristotle's Scientific Theory and Practice? Miira Tuominen 8. Two Traditions in the Ancient Posterior Analytics Commentaries Owen Goldin 9. Aristotle and Philoponus on Final Causes in Demonstrations in Posterior Analytics II 11 Mariska Leunissen 10. Aristotle on Causation and Conditional Necessity: Analytica Posteriora II 12 in Context Inna Kupreeva
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".